arXiv 28 Dec 2018 · Econometrics · 1 citations (OpenAlex)
arXiv:1812.11067 · PDF · DOI · OpenAlex · Extracted main text
Predicting future successful designs and corresponding market opportunity is a fundamental goal of product design firms. There is accordingly a long history of quantitative approaches that aim to capture diverse consumer preferences, and then translate those preferences to corresponding "design gaps" in the market. We extend this work by developing a deep learning approach to predict design gaps in the market. These design gaps represent clusters of designs that do not yet exist, but are predicted to be both (1) highly preferred by consumers, and (2) feasible to build under engineering and manufacturing constraints. This approach is tested on the entire U.S. automotive market using of millions of real purchase data. We retroactively predict design gaps in the market, and compare predicted design gaps with actual known successful designs. Our preliminary results give evidence it may be possible to predict design gaps, suggesting this approach has promise for early identification of market opportunity.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Peter E. Rossi and Greg M. Allenby (2003) Bayesian statistics and marketing | 0.843 | 3 | 3 | 100% |
| 2 | R Duncan Luce (1959) Individual choice behavior: A theoretical analysis | 0.737 | 3 | 2 | 100% |
| 3 | Alex Burnap, Yanxin Pan, Ye Liu, Yi Ren, Honglak Lee, Richard Gonzal… (2016) Improving Design Preference Prediction Accuracy Using Feature Learning self | 0.644 | 2 | 2 | 100% |
| 4 | John R Hauser, Olivier Toubia, Theodoros Evgeniou, Rene Befurt, and… (2010) Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets self | 0.644 | 2 | 2 | 100% |
| 5 | Peter J. Lenk, Wayne S. DeSarbo, Paul E. Green, and Martin R. Young (1996) Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs | 0.644 | 2 | 2 | 100% |
| 6 | Andriy Mnih and Ruslan Salakhutdinov (2007) Probabilistic matrix factorization | 0.644 | 2 | 2 | 100% |
| 7 | W. Ross Morrow, Minhua Long, and Erin F. MacDonald (2014) Market-System Design Optimization With Consider-Then-Choose Models | 0.644 | 2 | 2 | 100% |
| 8 | Bryan K Orme (2010) Getting started with conjoint analysis: strategies for product design and pricing research | 0.644 | 2 | 2 | 100% |
| 9 | John W. Payne (1976) Task complexity and contingent processing in decision making: An information search and protocol analysis | 0.644 | 2 | 2 | 100% |
| 10 | Louis Thurstone (1927) The method of paired comparisons for social values | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 53 scored citations.